Decoupling compound faults under data imbalance: A multi-task dynamic balanced learning network for gearbox fault diagnosis

Gearbox is a core component in mechanical transmission systems, and its accurate fault diagnosis is critical for ensuring operational safety of mechanical equipment. Although intelligent diagnosis methods have achieved promising results, they still face significant challenges. First, gearbox is a complex system with tightly coupled components (e.g., gears and bearings). Most existing methods focus on diagnosing isolated components, failing to effectively decouple and identify multi-component compound faults. Second, in actual engineering, fault data is usually scarce and severely imbalanced compared to normal data, which significantly degrades the performance of conventional data-driven models. To address these challenges, a multi-task dynamic balanced learning network is proposed for compound fault decoupling diagnosis with imbalanced data. Specifically, gearbox compound fault diagnosis is reformulated as a component-level multi-task diagnosis problem. First, a shared backbone network is designed to extract common features from vibration signals, followed by two kernel-adaptive task-specific branch networks that hierarchically disentangle component-level fault features for different tasks. Then, an adaptive balance loss is utilized to reweight training samples and alleviate the class imbalance influence. Finally, a task dynamic balancing strategy is incorporated to dynamically coordinate and optimize multiple diagnostic tasks during training. Extensive experiments are conducted on both real-world engineering and public datasets. The results show that the proposed method still achieves over 98% diagnostic accuracy on both gear and bearing diagnosis tasks even under severe class imbalance condition, demonstrating its superiority and robustness compared to other advanced methods.